Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells1170
Missing cells (%)8.4%
Duplicate rows2683
Duplicate rows (%)19.3%
Total size in memory216.9 KiB
Average record size in memory16.0 B

Variable types

TimeSeries1

Timeseries statistics

Number of series1
Time series length13880
Starting point1983-01-01 00:00:00
Ending point2020-12-31 00:00:00
Period1 day
2024-05-12T14:17:09.941859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T14:17:10.366604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 2683 (19.3%) duplicate rowsDuplicates
Flow has 1170 (8.4%) missing valuesMissing
Flow is non stationaryNon stationary
Flow is seasonalSeasonal

Reproduction

Analysis started2024-05-12 18:17:07.856384
Analysis finished2024-05-12 18:17:09.841622
Duration1.99 second
MissingQ_Station_NA_23187280_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING  NON STATIONARY  SEASONAL 

Distinct5447
Distinct (%)42.9%
Missing1170
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean3508.4183
Minimum724
Maximum8159
Zeros0
Zeros (%)0.0%
Memory size216.9 KiB
2024-05-12T14:17:11.253337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum724
5-th percentile1613
Q12424
median3282
Q34462.25
95-th percentile6046.55
Maximum8159
Range7435
Interquartile range (IQR)2038.25

Descriptive statistics

Standard deviation1366.1117
Coefficient of variation (CV)0.38938107
Kurtosis-0.50582805
Mean3508.4183
Median Absolute Deviation (MAD)988.5
Skewness0.49384678
Sum44591997
Variance1866261.2
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.55864136 × 10-21
2024-05-12T14:17:11.893862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T14:17:14.486120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps42
min3 days
max21 weeks and 5 days
mean4 weeks, 14 hours and 51 minutes
std5 weeks, 3 days and 19 hours
2024-05-12T14:17:14.951139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2996 17
 
0.1%
2396 16
 
0.1%
4010 14
 
0.1%
3403 14
 
0.1%
4698 14
 
0.1%
1955 14
 
0.1%
4647 14
 
0.1%
2040 13
 
0.1%
3242 13
 
0.1%
2115 13
 
0.1%
Other values (5437) 12568
90.5%
(Missing) 1170
 
8.4%
ValueCountFrequency (%)
724 1
< 0.1%
730.5 1
< 0.1%
737 1
< 0.1%
783.4 1
< 0.1%
807.8 1
< 0.1%
832.6 1
< 0.1%
836.2 1
< 0.1%
839.7 2
< 0.1%
860.9 1
< 0.1%
864.5 1
< 0.1%
ValueCountFrequency (%)
8159 1
< 0.1%
7726 1
< 0.1%
7650 1
< 0.1%
7605 1
< 0.1%
7594 1
< 0.1%
7583 1
< 0.1%
7537 2
< 0.1%
7526 2
< 0.1%
7481 1
< 0.1%
7480 1
< 0.1%
2024-05-12T14:17:13.723868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T14:17:09.215577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T14:17:09.565832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T14:17:09.752419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Flow
Date
1983-01-012327.0
1983-01-022259.0
1983-01-032236.0
1983-01-042397.0
1983-01-052404.0
1983-01-062064.0
1983-01-072174.0
1983-01-082739.0
1983-01-092687.0
1983-01-102398.0
Flow
Date
2020-12-223187.2
2020-12-233278.2
2020-12-243097.1
2020-12-252986.0
2020-12-263066.2
2020-12-273172.9
2020-12-283046.5
2020-12-293259.6
2020-12-303178.0
2020-12-312988.0

Duplicate rows

Most frequently occurring

Flow# duplicates
2682NaN1170
10292996.017
6032396.016
3181955.014
13153403.014
16724010.014
20194647.014
20444698.014
3692040.013
4172115.013